# Generative AI Training and Copyright Law

arXiv: https://arxiv.org/abs/2502.15858 · HTML: https://mlsanity.com/paper/2502.15858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Scalable Extraction of Training Data from (Production) Language Models](https://mlsanity.com/paper/2311.17035/) (2023)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [Detecting, Explaining, and Mitigating Memorization in Diffusion Models](https://mlsanity.com/paper/2407.21720/) (2024)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [Towards Memorization-Free Diffusion Models](https://mlsanity.com/paper/2404.00922/) (2024)
- [Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs](https://mlsanity.com/paper/2406.10209/) (2024)
- [A Geometric Framework for Understanding Memorization in Generative Models](https://mlsanity.com/paper/2411.00113/) (2024)
- [From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion](https://mlsanity.com/paper/2308.02560/) (2023)
- [AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks](https://mlsanity.com/paper/2401.06178/) (2024)
- [Frame by Familiar Frame: Understanding Replication in Video Diffusion Models](https://mlsanity.com/paper/2403.19593/) (2024)
- [Generation or Replication: Auscultating Audio Latent Diffusion Models](https://mlsanity.com/paper/2310.10604/) (2023)
- [SymPAC: Scalable Symbolic Music Generation With Prompts And Constraints](https://mlsanity.com/paper/2409.03055/) (2024)

## Built on by (most cited first, 3 of 3)

- [Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbation](https://mlsanity.com/paper/2602.01645/) (2026)
- [We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy](https://mlsanity.com/paper/2504.07936/) (2025)
- [Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance](https://mlsanity.com/paper/2605.15012/) (2026)

## Abstract (arXiv)

Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders.